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SAS Viya now includes built-in bias mitigation in its machine learning procedures to help users develop ethical and trustworthy AI models by automatically detecting and reducing bias during training.
This guide explains how businesses can successfully implement generative AI by focusing on narrow use cases, curating data, leveraging AI agents, safeguarding sensitive information, monitoring for bias and toxicity, and ensuring model accuracy and relevancy.
Accurately identifying lag structures between related time series is essential in public health forecasting, particularly during epidemics where delays between infections and hospitalizations affect planning. Using a simulated SEIR model and SAS Viya’s PROC TSSELECTLAG, distance correlation is shown to outperform Pearson correlation by correctly identifying nonlinear lag relationships—such as the true seven-day lag between new infections and hospital admissions.
As agentic AI systems evolve through protocols like MCP and A2A, traditional security practices must be adapted to address new risks such as goal misalignment and tool instruction abuse. This article explores practical threat modeling strategies, including goal alignment cascades and distinguishing between parameter-only vs. instruction-enabled tool calls.
In a previous article, I wrote about a mistake I made as a student in analytics. In short, my team handed off our training code and training data as the deliverable for our Capstone project, citing that the users can rerun our training code to get the model.
As described in our T&D World Article, SAS and our partner Exacter Inc. are helping utilities shift maintenance away from this reactive system towards a more efficient framework of condition-based maintenance (CBM).
They say nothing in life is certain other than death and taxes, but there something else I’ve found I can count on from experience: sending out invites for a party on social media only to receive a few affirmative responses and a whole slew of “maybe”.
As part of this year's IEEE Visual Analytics Science and Technology (VAST) Challenge, a group of SAS data scientists puit SAS Viya and related machine learning tools to the ultimate test - to identify individuals in a complex fishing network. Excitedly, the team received the Honorable Mention Award for Breadth of Investigation!
What sets the SAS Model Card apart from previous model cards is the use of descriptive visuals, to make model cards accessible to all personas involved in the analytics process, including data scientists, data engineers, MLOPs engineers, managers, executives, risk managers, business analytics, end-users, and any other stakeholder with access to the SAS Viya environment.
Learn how an intern integrated SAS Viya® and open-source code (Python) into a Machine Learning project to combine their strengths within the context of predictive modeling, and to show off the variety of ways this integration can be accomplished.
A recent article came out with an updated list of necessary components for MLOps and LLMOps. And while this list may seem long, reading through the capabilities and components, I realized that SAS Viya already covers most of the required functionality. Organizations can have a hodgepodge of tools that they